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Insights into Imaging
Fig. 1 | Insights into Imaging

Fig. 1

From: Generalizable attention U-Net for segmentation of fibroglandular tissue and background parenchymal enhancement in breast DCE-MRI

Fig. 1

Schematic representation of the model development pipeline. Two independent attention U-Net models are trained: the first one is trained to segment the fibroglandular tissue (FGT) and the fatty tissue from native DCE data; the second one is trained to segment BPE and non-enhancing tissue from the subtraction data. This separation ensures accurate segmentation even for not well-registered cases. In both cases, the segmentation is performed slice-wise ensuring that with the chosen hardware, the predicted mask has high resolution able to accurately capture the intricate details of the FGT and BPE structures (Icons made by Freepik and Netscript from flaticon.com)

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